Department of Geography, Faculty of Science, Masaryk University, 611 37 Brno, Czech Republic.
Agricultural Engineering Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
Sensors (Basel). 2021 Apr 23;21(9):2980. doi: 10.3390/s21092980.
Efforts related to minimizing the environmental burden caused by agricultural activities and increasing economic efficiency are key contemporary drivers in the precision agriculture domain. Controlled Traffic Farming (CTF) techniques are being applied against soil compaction creation, using the on-line optimization of trajectory planning for soil-sensitive field operations. The research presented in this paper aims at a proof-of-concept solution with respect to optimizing farm machinery trajectories in order to minimize the environmental burden and increase economic efficiency. As such, it further advances existing CTF solutions by including (1) efficient plot divisions in 3D, (2) the optimization of entry and exit points of both plot and plot segments, (3) the employment of more machines in parallel and (4) obstacles in a farm machinery trajectory. The developed algorithm is expressed in terms of unified modeling language (UML) activity diagrams as well as pseudo-code. Results were visualized in 2D and 3D to demonstrate terrain impact. Verifications were conducted at a fully operational commercial farm (Rostěnice, the Czech Republic) against second-by-second sensor measurements of real farm machinery trajectories.
努力将农业活动造成的环境负担最小化并提高经济效益是精准农业领域的当代主要驱动力。正在应用受控通行耕作（CTF）技术来防止土壤压实，使用土壤敏感田间作业的轨迹规划在线优化。本文的研究旨在针对优化农业机械轨迹以最小化环境负担并提高经济效益提供概念验证解决方案。因此，它通过包括（1）在 3D 中进行高效的地块划分，（2）优化地块和地块段的进出点，（3）并行使用更多机器以及（4）在农业机械轨迹中存在障碍物，进一步推进了现有的 CTF 解决方案。所开发的算法用统一建模语言（UML）活动图以及伪代码来表示。结果以 2D 和 3D 形式可视化，以展示地形影响。在一个完全运营的商业农场（捷克共和国的罗斯捷尼采）进行了验证，该农场使用实时农业机械轨迹的传感器进行逐秒测量。
